Detecting Financial Statement Irregularities: Evidence from the Distributional Properties of Financial Statement Numbers

• In Event: Measuring Accounting Quality

Abstract

Anecdotal evidence suggests that a significant portion of financial statement irregularities, whether created in error or to mislead, are ignored by reporting firms, their auditors, and the SEC. Motivated by a method used by forensic investigators and auditors to detect irregularities in a variety of settings, such as elections, tax return data, and individual financial accounts, we create a composite financial statement measure to estimate the degree of financial reporting irregularities for a given firm-year. The measure assesses the extent to which features of the distribution of a firm’s financial statement numbers diverge from a theoretical distribution posited by Benford’s Law, or the law of first digits. Whether in aggregate, by year, or by industry, we find that the empirical distribution of the numbers in firms’ financial reports generally conform to the theoretical distribution specified by Benford’s Law. In a battery of construct validity tests, we show that i) the divergence measure is positively correlated with commonly used earnings management proxies, ii) the restated financial reports of misstating firms exhibit greater conformity, and iii) divergence decreases in the years following restatements. Turning to the informational implications of Benford’s Law, we provide evidence that as divergence increases, information asymmetry increases and earnings persistence decreases in the year following the disclosure of the financial report. These results suggest that the degree of divergence from Benford’s Law can be used as a tool to detect possible financial irregularities.

Authors

• Dan Amiram, Columbia University

• Zahn Bozanic, The Ohio State University

• Ethan Rouen, Columbia University